Overview

Dataset statistics

Number of variables30
Number of observations34706
Missing cells260957
Missing cells (%)25.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 MiB
Average record size in memory240.0 B

Variable types

Text1
Categorical4
DateTime1
Numeric24

Alerts

act_on_foot_ep_0 has 27505 (79.3%) missing valuesMissing
loc_study_still has 18911 (54.5%) missing valuesMissing
loc_study_product has 18911 (54.5%) missing valuesMissing
loc_dist_ep_0 has 4583 (13.2%) missing valuesMissing
loc_visit_num_ep_0 has 3532 (10.2%) missing valuesMissing
loc_social_unlock_duration has 21735 (62.6%) missing valuesMissing
loc_food_unlock_duration has 18553 (53.5%) missing valuesMissing
loc_social_unlock_num has 21735 (62.6%) missing valuesMissing
loc_food_unlock_num has 18553 (53.5%) missing valuesMissing
loc_social_still has 21735 (62.6%) missing valuesMissing
loc_food_still has 18553 (53.5%) missing valuesMissing
loc_social_unlock_duration_plus_loc_food_unlock_duration has 21893 (63.1%) missing valuesMissing
loc_social_unlock_num_plus_loc_food_unlock_num has 21893 (63.1%) missing valuesMissing
loc_social_still_plus_loc_food_still has 21893 (63.1%) missing valuesMissing
loc_dist_ep_0 is highly skewed (γ1 = 51.10741875)Skewed
act_running_ep_0 has 7944 (22.9%) zerosZeros
act_walking_ep_0 has 7479 (21.5%) zerosZeros
loc_study_dur has 18754 (54.0%) zerosZeros
loc_home_dur has 6787 (19.6%) zerosZeros
loc_workout_dur has 25384 (73.1%) zerosZeros
act_in_vehicle_ep_0 has 1811 (5.2%) zerosZeros
loc_social_dur has 21578 (62.2%) zerosZeros
loc_food_dur has 18396 (53.0%) zerosZeros
loc_visit_num_ep_0 has 1479 (4.3%) zerosZeros
loc_social_unlock_duration has 626 (1.8%) zerosZeros
loc_food_unlock_duration has 694 (2.0%) zerosZeros
loc_social_unlock_num has 626 (1.8%) zerosZeros
loc_food_unlock_num has 694 (2.0%) zerosZeros
act_running_ep_0_plus_act_walking_ep_0 has 7479 (21.5%) zerosZeros
loc_social_dur_plus_loc_food_dur has 18238 (52.5%) zerosZeros
loc_social_unlock_duration_plus_loc_food_unlock_duration has 488 (1.4%) zerosZeros
loc_social_unlock_num_plus_loc_food_unlock_num has 488 (1.4%) zerosZeros

Reproduction

Analysis started2024-05-23 19:05:02.778369
Analysis finished2024-05-23 19:05:45.061133
Duration42.28 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

uid
Text

Distinct220
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:45.313929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters1110592
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row3569e2f520db9014b4acc4227a6421c1
2nd row3569e2f520db9014b4acc4227a6421c1
3rd row3569e2f520db9014b4acc4227a6421c1
4th row3569e2f520db9014b4acc4227a6421c1
5th row3569e2f520db9014b4acc4227a6421c1
ValueCountFrequency (%)
aeeb186fafcc356f44cae870555f4a0d 441
 
1.3%
862f10a8c357e957a59d122077b3a5ad 436
 
1.3%
c37f9221f44e9ca35a49180dc05a7587 417
 
1.2%
73e13f8273906f7f43a077f95ec48e7d 388
 
1.1%
7d2c632a05bbb03ca97555d61be83c41 383
 
1.1%
1d2263527eed2a54e88d340fb8e55308 377
 
1.1%
35cf1abf179310dc33907d953f590366 372
 
1.1%
46b53cdf4d639d54e894d92b6dff817f 349
 
1.0%
bd6de07de02a8c2e98018a1c7daecc87 344
 
1.0%
ffc4b142e017c162ed4db7b05414fc4b 335
 
1.0%
Other values (210) 30864
88.9%
2024-05-23T12:05:45.709061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 79812
 
7.2%
3 73025
 
6.6%
5 71714
 
6.5%
1 71009
 
6.4%
d 70849
 
6.4%
7 70286
 
6.3%
c 69623
 
6.3%
a 69321
 
6.2%
e 68788
 
6.2%
4 68442
 
6.2%
Other values (6) 397723
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1110592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 79812
 
7.2%
3 73025
 
6.6%
5 71714
 
6.5%
1 71009
 
6.4%
d 70849
 
6.4%
7 70286
 
6.3%
c 69623
 
6.3%
a 69321
 
6.2%
e 68788
 
6.2%
4 68442
 
6.2%
Other values (6) 397723
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1110592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 79812
 
7.2%
3 73025
 
6.6%
5 71714
 
6.5%
1 71009
 
6.4%
d 70849
 
6.4%
7 70286
 
6.3%
c 69623
 
6.3%
a 69321
 
6.2%
e 68788
 
6.2%
4 68442
 
6.2%
Other values (6) 397723
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1110592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 79812
 
7.2%
3 73025
 
6.6%
5 71714
 
6.5%
1 71009
 
6.4%
d 70849
 
6.4%
7 70286
 
6.3%
c 69623
 
6.3%
a 69321
 
6.2%
e 68788
 
6.2%
4 68442
 
6.2%
Other values (6) 397723
35.8%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size271.3 KiB
F
23438 
M
10990 
both
 
274

Length

Max length4
Median length1
Mean length1.0236874
Min length1

Characters and Unicode

Total characters35524
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowboth
2nd rowboth
3rd rowboth
4th rowboth
5th rowboth

Common Values

ValueCountFrequency (%)
F 23438
67.5%
M 10990
31.7%
both 274
 
0.8%
(Missing) 4
 
< 0.1%

Length

2024-05-23T12:05:45.838952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T12:05:45.911565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
f 23438
67.5%
m 10990
31.7%
both 274
 
0.8%

Most occurring characters

ValueCountFrequency (%)
F 23438
66.0%
M 10990
30.9%
b 274
 
0.8%
o 274
 
0.8%
t 274
 
0.8%
h 274
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 23438
66.0%
M 10990
30.9%
b 274
 
0.8%
o 274
 
0.8%
t 274
 
0.8%
h 274
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 23438
66.0%
M 10990
30.9%
b 274
 
0.8%
o 274
 
0.8%
t 274
 
0.8%
h 274
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 23438
66.0%
M 10990
30.9%
b 274
 
0.8%
o 274
 
0.8%
t 274
 
0.8%
h 274
 
0.8%

race
Categorical

Distinct8
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size271.3 KiB
white
20817 
asian
9002 
more than one
 
1475
other/hispanic
 
1364
black
 
1334
Other values (3)
 
710

Length

Max length29
Median length5
Mean length6.0893608
Min length5

Characters and Unicode

Total characters211313
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 20817
60.0%
asian 9002
25.9%
more than one 1475
 
4.2%
other/hispanic 1364
 
3.9%
black 1334
 
3.8%
american indian/alaska native 322
 
0.9%
alaskan native/white 209
 
0.6%
american indian/white 179
 
0.5%
(Missing) 4
 
< 0.1%

Length

2024-05-23T12:05:45.982084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T12:05:46.055919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
white 20817
53.8%
asian 9002
23.3%
more 1475
 
3.8%
than 1475
 
3.8%
one 1475
 
3.8%
other/hispanic 1364
 
3.5%
black 1334
 
3.4%
american 501
 
1.3%
indian/alaska 322
 
0.8%
native 322
 
0.8%
Other values (3) 597
 
1.5%

Most occurring characters

ValueCountFrequency (%)
i 34969
16.5%
e 26551
12.6%
a 25804
12.2%
h 25408
12.0%
t 24575
11.6%
w 21205
10.0%
n 15559
7.4%
s 10897
 
5.2%
o 4314
 
2.0%
3982
 
1.9%
Other values (10) 18049
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 211313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 34969
16.5%
e 26551
12.6%
a 25804
12.2%
h 25408
12.0%
t 24575
11.6%
w 21205
10.0%
n 15559
7.4%
s 10897
 
5.2%
o 4314
 
2.0%
3982
 
1.9%
Other values (10) 18049
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 211313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 34969
16.5%
e 26551
12.6%
a 25804
12.2%
h 25408
12.0%
t 24575
11.6%
w 21205
10.0%
n 15559
7.4%
s 10897
 
5.2%
o 4314
 
2.0%
3982
 
1.9%
Other values (10) 18049
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 211313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 34969
16.5%
e 26551
12.6%
a 25804
12.2%
h 25408
12.0%
t 24575
11.6%
w 21205
10.0%
n 15559
7.4%
s 10897
 
5.2%
o 4314
 
2.0%
3982
 
1.9%
Other values (10) 18049
8.5%

cohort_year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size271.3 KiB
2018.0
17598 
2017.0
17106 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters208224
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017.0
2nd row2017.0
3rd row2017.0
4th row2017.0
5th row2017.0

Common Values

ValueCountFrequency (%)
2018.0 17598
50.7%
2017.0 17106
49.3%
(Missing) 2
 
< 0.1%

Length

2024-05-23T12:05:46.236478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T12:05:46.296281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2018.0 17598
50.7%
2017.0 17106
49.3%

Most occurring characters

ValueCountFrequency (%)
0 69408
33.3%
2 34704
16.7%
1 34704
16.7%
. 34704
16.7%
8 17598
 
8.5%
7 17106
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 208224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 69408
33.3%
2 34704
16.7%
1 34704
16.7%
. 34704
16.7%
8 17598
 
8.5%
7 17106
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 208224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 69408
33.3%
2 34704
16.7%
1 34704
16.7%
. 34704
16.7%
8 17598
 
8.5%
7 17106
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 208224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 69408
33.3%
2 34704
16.7%
1 34704
16.7%
. 34704
16.7%
8 17598
 
8.5%
7 17106
 
8.2%

is_ios
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size271.3 KiB
1
26711 
0
7995 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34706
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 26711
77.0%
0 7995
 
23.0%

Length

2024-05-23T12:05:46.364797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T12:05:46.424319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 26711
77.0%
0 7995
 
23.0%

Most occurring characters

ValueCountFrequency (%)
1 26711
77.0%
0 7995
 
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34706
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 26711
77.0%
0 7995
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34706
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 26711
77.0%
0 7995
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34706
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 26711
77.0%
0 7995
 
23.0%
Distinct1749
Distinct (%)5.0%
Missing4
Missing (%)< 0.1%
Memory size271.3 KiB
Minimum2017-09-08 00:00:00
Maximum2022-06-25 00:00:00
2024-05-23T12:05:46.498857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:46.593928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

act_on_foot_ep_0
Real number (ℝ)

MISSING 

Distinct6795
Distinct (%)94.4%
Missing27505
Missing (%)79.3%
Infinite0
Infinite (%)0.0%
Mean2948.364
Minimum0
Maximum36290
Zeros44
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:46.688733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile124.8
Q11058.2
median2503.6
Q34437.9
95-th percentile7063.8
Maximum36290
Range36290
Interquartile range (IQR)3379.7

Descriptive statistics

Standard deviation2343.9692
Coefficient of variation (CV)0.79500671
Kurtosis7.1348506
Mean2948.364
Median Absolute Deviation (MAD)1613.1
Skewness1.390506
Sum21231169
Variance5494191.4
MonotonicityNot monotonic
2024-05-23T12:05:46.778520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44
 
0.1%
56.3 4
 
< 0.1%
1.6 4
 
< 0.1%
181.1 4
 
< 0.1%
12.3 3
 
< 0.1%
364.7 3
 
< 0.1%
1787.4 3
 
< 0.1%
2342.3 3
 
< 0.1%
45.5 3
 
< 0.1%
32.7 3
 
< 0.1%
Other values (6785) 7127
 
20.5%
(Missing) 27505
79.3%
ValueCountFrequency (%)
0 44
0.1%
0.5555555556 1
 
< 0.1%
0.7 1
 
< 0.1%
0.8 2
 
< 0.1%
1.2 2
 
< 0.1%
1.5 2
 
< 0.1%
1.6 4
 
< 0.1%
2 1
 
< 0.1%
2.2 1
 
< 0.1%
2.8 1
 
< 0.1%
ValueCountFrequency (%)
36290 1
< 0.1%
22191 1
< 0.1%
18041.5 1
< 0.1%
17323.28571 1
< 0.1%
14772 1
< 0.1%
13174.5 1
< 0.1%
13062.2 1
< 0.1%
12938.3 1
< 0.1%
12647.8 1
< 0.1%
12538.3 1
< 0.1%

act_running_ep_0
Real number (ℝ)

ZEROS 

Distinct5468
Distinct (%)15.8%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean94.745873
Minimum0
Maximum4608
Zeros7944
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:46.867190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.3
median23.8
Q389.1
95-th percentile434.495
Maximum4608
Range4608
Interquartile range (IQR)87.8

Descriptive statistics

Standard deviation206.48418
Coefficient of variation (CV)2.1793475
Kurtosis40.666592
Mean94.745873
Median Absolute Deviation (MAD)23.8
Skewness5.1206615
Sum3287871.3
Variance42635.715
MonotonicityNot monotonic
2024-05-23T12:05:46.951279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7944
 
22.9%
0.5 92
 
0.3%
1 87
 
0.3%
0.3 80
 
0.2%
3 74
 
0.2%
2.5 73
 
0.2%
4.4 71
 
0.2%
1.1 67
 
0.2%
1.8 67
 
0.2%
0.8 67
 
0.2%
Other values (5458) 26080
75.1%
ValueCountFrequency (%)
0 7944
22.9%
0.2 65
 
0.2%
0.3 80
 
0.2%
0.3333333333 1
 
< 0.1%
0.375 1
 
< 0.1%
0.4 27
 
0.1%
0.4285714286 1
 
< 0.1%
0.5 92
 
0.3%
0.5555555556 1
 
< 0.1%
0.6 35
 
0.1%
ValueCountFrequency (%)
4608 1
< 0.1%
3189.3 1
< 0.1%
3169.25 1
< 0.1%
3031.9 1
< 0.1%
3009.9 1
< 0.1%
2952.6 1
< 0.1%
2889.3 1
< 0.1%
2865.1 1
< 0.1%
2843.6 1
< 0.1%
2700.555556 1
< 0.1%

act_walking_ep_0
Real number (ℝ)

ZEROS 

Distinct25117
Distinct (%)72.4%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean8759.1977
Minimum0
Maximum43022
Zeros7479
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:47.034430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13883.25
median9375.75
Q313127.425
95-th percentile18391.035
Maximum43022
Range43022
Interquartile range (IQR)9244.175

Descriptive statistics

Standard deviation6111.7136
Coefficient of variation (CV)0.6977481
Kurtosis-0.70743031
Mean8759.1977
Median Absolute Deviation (MAD)4233.95
Skewness0.035507578
Sum3.0396168 × 108
Variance37353043
MonotonicityNot monotonic
2024-05-23T12:05:47.118223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7479
 
21.5%
8086.5 5
 
< 0.1%
12734.3 4
 
< 0.1%
13800.2 4
 
< 0.1%
10982 4
 
< 0.1%
8250.8 4
 
< 0.1%
6004.8 4
 
< 0.1%
10273 3
 
< 0.1%
15279.4 3
 
< 0.1%
9676.1 3
 
< 0.1%
Other values (25107) 27189
78.3%
(Missing) 4
 
< 0.1%
ValueCountFrequency (%)
0 7479
21.5%
0.5 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.9 1
 
< 0.1%
1.333333333 2
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 1
 
< 0.1%
3.833333333 1
 
< 0.1%
5.9 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
43022 1
< 0.1%
32727.3 1
< 0.1%
32642.2 1
< 0.1%
32080.2 1
< 0.1%
31821.1 1
< 0.1%
31330.6 1
< 0.1%
30823.7 1
< 0.1%
30736 1
< 0.1%
30453.9 1
< 0.1%
30286.4 1
< 0.1%

loc_study_still
Real number (ℝ)

MISSING 

Distinct15313
Distinct (%)96.9%
Missing18911
Missing (%)54.5%
Infinite0
Infinite (%)0.0%
Mean51.242227
Minimum3.275766
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:47.198522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.275766
5-th percentile38.516243
Q148.824501
median53.165647
Q355.404126
95-th percentile58.979626
Maximum60
Range56.724234
Interquartile range (IQR)6.5796247

Descriptive statistics

Standard deviation6.6848257
Coefficient of variation (CV)0.13045541
Kurtosis6.1656948
Mean51.242227
Median Absolute Deviation (MAD)2.8246178
Skewness-1.9591248
Sum809370.98
Variance44.686894
MonotonicityNot monotonic
2024-05-23T12:05:47.280229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.98571769 5
 
< 0.1%
55.98437584 5
 
< 0.1%
49.13896877 5
 
< 0.1%
42 4
 
< 0.1%
39.39126983 4
 
< 0.1%
46.83555068 4
 
< 0.1%
15.01840491 4
 
< 0.1%
16.4627583 4
 
< 0.1%
53.3502609 4
 
< 0.1%
32.84087753 4
 
< 0.1%
Other values (15303) 15752
45.4%
(Missing) 18911
54.5%
ValueCountFrequency (%)
3.275766017 1
 
< 0.1%
3.866666667 2
< 0.1%
5.67966017 1
 
< 0.1%
6.866666667 2
< 0.1%
6.885775862 1
 
< 0.1%
7.162687396 2
< 0.1%
7.482013497 3
< 0.1%
7.948717949 1
 
< 0.1%
8.957055215 1
 
< 0.1%
9.694614103 1
 
< 0.1%
ValueCountFrequency (%)
60 2
< 0.1%
59.9972639 1
< 0.1%
59.99642725 1
< 0.1%
59.9960692 1
< 0.1%
59.99606798 1
< 0.1%
59.99598963 1
< 0.1%
59.99595407 1
< 0.1%
59.99584053 1
< 0.1%
59.99583391 1
< 0.1%
59.99583362 1
< 0.1%

loc_study_dur
Real number (ℝ)

ZEROS 

Distinct14840
Distinct (%)43.0%
Missing157
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.6377419
Minimum0
Maximum23.002912
Zeros18754
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:47.364617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.6044722
95-th percentile6.5410454
Maximum23.002912
Range23.002912
Interquartile range (IQR)2.6044722

Descriptive statistics

Standard deviation2.8707786
Coefficient of variation (CV)1.7528883
Kurtosis11.858651
Mean1.6377419
Median Absolute Deviation (MAD)0
Skewness2.9667358
Sum56582.345
Variance8.24137
MonotonicityNot monotonic
2024-05-23T12:05:47.447855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18754
54.0%
0.05 33
 
0.1%
0.1666944444 24
 
0.1%
0.1166666667 18
 
0.1%
0.08336111111 16
 
< 0.1%
0.2000277778 15
 
< 0.1%
0.1166944444 14
 
< 0.1%
0.1000277778 12
 
< 0.1%
0.06669444444 12
 
< 0.1%
0.06666666667 12
 
< 0.1%
Other values (14830) 15639
45.1%
(Missing) 157
 
0.5%
ValueCountFrequency (%)
0 18754
54.0%
0.01666666667 1
 
< 0.1%
0.03333333333 2
 
< 0.1%
0.03336111111 1
 
< 0.1%
0.03361111111 2
 
< 0.1%
0.03427777778 2
 
< 0.1%
0.03441666667 1
 
< 0.1%
0.03461111111 1
 
< 0.1%
0.03477777778 2
 
< 0.1%
0.03513888889 1
 
< 0.1%
ValueCountFrequency (%)
23.00291222 1
< 0.1%
22.10058333 1
< 0.1%
21.96547222 1
< 0.1%
21.73336111 1
< 0.1%
21.68663889 1
< 0.1%
21.63622222 1
< 0.1%
21.53913889 1
< 0.1%
21.25377778 1
< 0.1%
21.23377683 1
< 0.1%
21.14190631 1
< 0.1%

loc_study_product
Real number (ℝ)

MISSING 

Distinct15315
Distinct (%)97.0%
Missing18911
Missing (%)54.5%
Infinite0
Infinite (%)0.0%
Mean251.03706
Minimum1.6333333
Maximum1364.058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:47.529982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.6333333
5-th percentile66.770515
Q1143.52761
median208.87083
Q3295.0036
95-th percentile638.92783
Maximum1364.058
Range1362.4246
Interquartile range (IQR)151.476

Descriptive statistics

Standard deviation184.69405
Coefficient of variation (CV)0.73572423
Kurtosis7.4695899
Mean251.03706
Median Absolute Deviation (MAD)73.102083
Skewness2.4501346
Sum3965130.3
Variance34111.891
MonotonicityNot monotonic
2024-05-23T12:05:47.624363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177.6916667 5
 
< 0.1%
70 5
 
< 0.1%
76.14166667 5
 
< 0.1%
10.20125571 4
 
< 0.1%
32.85 4
 
< 0.1%
61.44084831 4
 
< 0.1%
21.35288167 4
 
< 0.1%
175.5405088 4
 
< 0.1%
77.69166667 4
 
< 0.1%
29.8411719 4
 
< 0.1%
Other values (15305) 15752
45.4%
(Missing) 18911
54.5%
ValueCountFrequency (%)
1.633333333 1
 
< 0.1%
1.933333333 2
< 0.1%
3.1 1
 
< 0.1%
3.433333333 2
< 0.1%
3.583333333 2
< 0.1%
3.9 2
< 0.1%
4 1
 
< 0.1%
4.333413872 3
< 0.1%
4.816666667 1
 
< 0.1%
4.85 1
 
< 0.1%
ValueCountFrequency (%)
1364.057973 1
< 0.1%
1329.548283 1
< 0.1%
1311.631716 1
< 0.1%
1302.373456 1
< 0.1%
1291.799965 1
< 0.1%
1290.447028 1
< 0.1%
1285.600856 1
< 0.1%
1281.666667 1
< 0.1%
1276.56 1
< 0.1%
1268.251341 1
< 0.1%

loc_home_dur
Real number (ℝ)

ZEROS 

Distinct27578
Distinct (%)79.8%
Missing157
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean9.751511
Minimum0
Maximum23.981444
Zeros6787
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:47.715018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.7496944
median10.752083
Q314.643222
95-th percentile20.089128
Maximum23.981444
Range23.981444
Interquartile range (IQR)10.893528

Descriptive statistics

Standard deviation6.6300403
Coefficient of variation (CV)0.67989876
Kurtosis-1.0317415
Mean9.751511
Median Absolute Deviation (MAD)4.78625
Skewness-0.12397822
Sum336904.95
Variance43.957434
MonotonicityNot monotonic
2024-05-23T12:05:47.805064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6787
 
19.6%
0.05 4
 
< 0.1%
3.020305556 4
 
< 0.1%
0.1704258611 4
 
< 0.1%
0.1705490556 4
 
< 0.1%
1.673934389 4
 
< 0.1%
1.2495 3
 
< 0.1%
9.223555556 3
 
< 0.1%
2.249889 3
 
< 0.1%
3.044055556 3
 
< 0.1%
Other values (27568) 27730
79.9%
(Missing) 157
 
0.5%
ValueCountFrequency (%)
0 6787
19.6%
0.01666666667 1
 
< 0.1%
0.03327777778 1
 
< 0.1%
0.03330555556 1
 
< 0.1%
0.03375155556 1
 
< 0.1%
0.03380461111 1
 
< 0.1%
0.03447222222 2
 
< 0.1%
0.03658333333 1
 
< 0.1%
0.03944444444 1
 
< 0.1%
0.03961111111 1
 
< 0.1%
ValueCountFrequency (%)
23.98144444 1
< 0.1%
23.97108333 1
< 0.1%
23.97080556 1
< 0.1%
23.9443585 1
< 0.1%
23.93147222 1
< 0.1%
23.92102778 1
< 0.1%
23.90494444 1
< 0.1%
23.90466667 1
< 0.1%
23.90455556 1
< 0.1%
23.90455556 1
< 0.1%

loc_workout_dur
Real number (ℝ)

ZEROS 

Distinct6538
Distinct (%)18.9%
Missing157
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.15353227
Minimum0
Maximum14.685222
Zeros25384
Zeros (%)73.1%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:47.889799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.066666667
95-th percentile0.87479444
Maximum14.685222
Range14.685222
Interquartile range (IQR)0.066666667

Descriptive statistics

Standard deviation0.46047347
Coefficient of variation (CV)2.9991966
Kurtosis104.85233
Mean0.15353227
Median Absolute Deviation (MAD)0
Skewness7.1917398
Sum5304.3864
Variance0.21203582
MonotonicityNot monotonic
2024-05-23T12:05:47.980629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25384
73.1%
0.06666666667 70
 
0.2%
0.05 67
 
0.2%
0.1166944444 60
 
0.2%
0.08333333333 53
 
0.2%
0.06669444444 51
 
0.1%
0.1 41
 
0.1%
0.15 38
 
0.1%
0.08336111111 38
 
0.1%
0.06663888889 37
 
0.1%
Other values (6528) 8710
 
25.1%
(Missing) 157
 
0.5%
ValueCountFrequency (%)
0 25384
73.1%
0.01666716667 1
 
< 0.1%
0.03319444444 1
 
< 0.1%
0.03325 1
 
< 0.1%
0.03333333333 1
 
< 0.1%
0.03336111111 1
 
< 0.1%
0.03391666667 2
 
< 0.1%
0.03444444444 1
 
< 0.1%
0.03452777778 2
 
< 0.1%
0.03469444444 2
 
< 0.1%
ValueCountFrequency (%)
14.68522222 1
< 0.1%
12.69905556 1
< 0.1%
12.44058333 1
< 0.1%
10.28533333 1
< 0.1%
10.06680556 1
< 0.1%
8.640944444 1
< 0.1%
6.978666667 1
< 0.1%
6.862111111 1
< 0.1%
6.428333333 1
< 0.1%
6.181055556 1
< 0.1%

act_in_vehicle_ep_0
Real number (ℝ)

ZEROS 

Distinct23471
Distinct (%)67.6%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2172.9635
Minimum0
Maximum25374.3
Zeros1811
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:48.070256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1288
median1403.3
Q33344.65
95-th percentile6762.61
Maximum25374.3
Range25374.3
Interquartile range (IQR)3056.65

Descriptive statistics

Standard deviation2387.247
Coefficient of variation (CV)1.0986135
Kurtosis5.4819674
Mean2172.9635
Median Absolute Deviation (MAD)1259.25
Skewness1.8113631
Sum75406178
Variance5698948.3
MonotonicityNot monotonic
2024-05-23T12:05:48.251985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1811
 
5.2%
19.5 19
 
0.1%
1.5 17
 
< 0.1%
5 15
 
< 0.1%
32.9 14
 
< 0.1%
3 12
 
< 0.1%
8.5 12
 
< 0.1%
1.6 12
 
< 0.1%
7.8 11
 
< 0.1%
18 11
 
< 0.1%
Other values (23461) 32768
94.4%
ValueCountFrequency (%)
0 1811
5.2%
0.3 3
 
< 0.1%
0.4 3
 
< 0.1%
0.5 2
 
< 0.1%
0.6 2
 
< 0.1%
0.6666666667 1
 
< 0.1%
0.7 1
 
< 0.1%
0.8 4
 
< 0.1%
0.9 4
 
< 0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
25374.3 1
< 0.1%
24673.4 1
< 0.1%
24092.2 1
< 0.1%
23886.5 1
< 0.1%
23354.2 1
< 0.1%
22334.4 1
< 0.1%
22123.7 1
< 0.1%
21248 1
< 0.1%
21071.5 1
< 0.1%
20450.9 1
< 0.1%

loc_dist_ep_0
Real number (ℝ)

MISSING  SKEWED 

Distinct29628
Distinct (%)98.4%
Missing4583
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean164481.14
Minimum0
Maximum1.4242156 × 108
Zeros267
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:48.334550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1159.08
Q15482.391
median22575.538
Q375181.498
95-th percentile463231.3
Maximum1.4242156 × 108
Range1.4242156 × 108
Interquartile range (IQR)69699.107

Descriptive statistics

Standard deviation1867988.4
Coefficient of variation (CV)11.356855
Kurtosis3493.9396
Mean164481.14
Median Absolute Deviation (MAD)19557.114
Skewness51.107419
Sum4.9546653 × 109
Variance3.4893807 × 1012
MonotonicityNot monotonic
2024-05-23T12:05:48.425178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 267
 
0.8%
417.376509 6
 
< 0.1%
73.81279146 4
 
< 0.1%
0.8144253537 4
 
< 0.1%
9.796842445 3
 
< 0.1%
9.321555283 3
 
< 0.1%
11.48979321 3
 
< 0.1%
137.1731203 3
 
< 0.1%
2154.098184 3
 
< 0.1%
4773.407923 3
 
< 0.1%
Other values (29618) 29824
85.9%
(Missing) 4583
 
13.2%
ValueCountFrequency (%)
0 267
0.8%
0.3152189938 1
 
< 0.1%
0.3699870183 2
 
< 0.1%
0.3751709523 2
 
< 0.1%
0.8136327639 1
 
< 0.1%
0.8144253537 4
 
< 0.1%
1.032295243 2
 
< 0.1%
1.124663488 1
 
< 0.1%
1.371707153 1
 
< 0.1%
1.413705336 1
 
< 0.1%
ValueCountFrequency (%)
142421562.1 1
< 0.1%
142391822.1 1
< 0.1%
142380475.7 1
< 0.1%
55951647.87 1
< 0.1%
55914254.36 1
< 0.1%
42122126.02 1
< 0.1%
42121761.16 1
< 0.1%
42120759.64 1
< 0.1%
41827623.8 1
< 0.1%
41827212.35 1
< 0.1%

loc_social_dur
Real number (ℝ)

ZEROS 

Distinct9585
Distinct (%)27.7%
Missing157
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.25947381
Minimum0
Maximum10.9825
Zeros21578
Zeros (%)62.2%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:48.517226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.24334322
95-th percentile1.32685
Maximum10.9825
Range10.9825
Interquartile range (IQR)0.24334322

Descriptive statistics

Standard deviation0.64604107
Coefficient of variation (CV)2.4898123
Kurtosis39.823544
Mean0.25947381
Median Absolute Deviation (MAD)0
Skewness5.180178
Sum8964.5605
Variance0.41736906
MonotonicityNot monotonic
2024-05-23T12:05:48.603665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21578
62.2%
0.05 162
 
0.5%
0.06666666667 81
 
0.2%
0.08333333333 73
 
0.2%
0.05002777778 67
 
0.2%
0.06669444444 67
 
0.2%
0.1 55
 
0.2%
0.1000277778 46
 
0.1%
0.06663888889 44
 
0.1%
0.1166666667 36
 
0.1%
Other values (9575) 12340
35.6%
(Missing) 157
 
0.5%
ValueCountFrequency (%)
0 21578
62.2%
0.01666666667 4
 
< 0.1%
0.03430555556 1
 
< 0.1%
0.03433333333 3
 
< 0.1%
0.03438888889 4
 
< 0.1%
0.03444444444 1
 
< 0.1%
0.0345 1
 
< 0.1%
0.03463888889 1
 
< 0.1%
0.03502777778 1
 
< 0.1%
0.03513888889 1
 
< 0.1%
ValueCountFrequency (%)
10.9825 1
< 0.1%
10.34816667 1
< 0.1%
10.00694444 1
< 0.1%
9.961413111 1
< 0.1%
9.357526139 1
< 0.1%
9.043095583 1
< 0.1%
8.75268525 1
< 0.1%
8.336103972 1
< 0.1%
8.220094056 1
< 0.1%
8.083534611 1
< 0.1%

loc_food_dur
Real number (ℝ)

ZEROS 

Distinct14794
Distinct (%)42.8%
Missing157
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.53576831
Minimum0
Maximum17.379833
Zeros18396
Zeros (%)53.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:48.687757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.84897222
95-th percentile2.1031294
Maximum17.379833
Range17.379833
Interquartile range (IQR)0.84897222

Descriptive statistics

Standard deviation1.0007971
Coefficient of variation (CV)1.8679663
Kurtosis54.168637
Mean0.53576831
Median Absolute Deviation (MAD)0
Skewness5.3963185
Sum18510.259
Variance1.0015949
MonotonicityNot monotonic
2024-05-23T12:05:48.775541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18396
53.0%
0.05 46
 
0.1%
0.06666666667 28
 
0.1%
0.06669444444 24
 
0.1%
0.1166666667 21
 
0.1%
0.1666944444 20
 
0.1%
0.08333333333 19
 
0.1%
0.1166944444 16
 
< 0.1%
0.08336111111 15
 
< 0.1%
0.1 14
 
< 0.1%
Other values (14784) 15950
46.0%
(Missing) 157
 
0.5%
ValueCountFrequency (%)
0 18396
53.0%
0.01687447222 1
 
< 0.1%
0.03288888889 2
 
< 0.1%
0.03333333333 1
 
< 0.1%
0.03438888889 1
 
< 0.1%
0.03502777778 1
 
< 0.1%
0.03505555556 2
 
< 0.1%
0.03616666667 2
 
< 0.1%
0.03691358025 1
 
< 0.1%
0.037 1
 
< 0.1%
ValueCountFrequency (%)
17.37983333 1
< 0.1%
16.19619444 1
< 0.1%
15.93075 1
< 0.1%
15.79025 1
< 0.1%
15.63955556 1
< 0.1%
15.48269444 1
< 0.1%
15.29466667 1
< 0.1%
15.03952778 1
< 0.1%
15.01219444 1
< 0.1%
14.88116667 1
< 0.1%

loc_visit_num_ep_0
Real number (ℝ)

MISSING  ZEROS 

Distinct275
Distinct (%)0.9%
Missing3532
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean3.4101619
Minimum0
Maximum11.3
Zeros1479
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:48.864932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.33333333
Q11.8
median3.3
Q34.9
95-th percentile6.6
Maximum11.3
Range11.3
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation1.9159857
Coefficient of variation (CV)0.56184596
Kurtosis-0.67241027
Mean3.4101619
Median Absolute Deviation (MAD)1.5
Skewness0.21288477
Sum106308.39
Variance3.6710011
MonotonicityNot monotonic
2024-05-23T12:05:48.950441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1505
 
4.3%
0 1479
 
4.3%
1.1 700
 
2.0%
1.3 619
 
1.8%
4 617
 
1.8%
1.2 578
 
1.7%
2 572
 
1.6%
3 547
 
1.6%
3.5 540
 
1.6%
3.1 532
 
1.5%
Other values (265) 23485
67.7%
(Missing) 3532
 
10.2%
ValueCountFrequency (%)
0 1479
4.3%
0.1 15
 
< 0.1%
0.1111111111 4
 
< 0.1%
0.125 3
 
< 0.1%
0.1428571429 2
 
< 0.1%
0.1666666667 6
 
< 0.1%
0.2 14
 
< 0.1%
0.2222222222 3
 
< 0.1%
0.25 20
 
0.1%
0.2857142857 3
 
< 0.1%
ValueCountFrequency (%)
11.3 2
< 0.1%
11.2 2
< 0.1%
11 2
< 0.1%
10.9 2
< 0.1%
10.8 1
 
< 0.1%
10.5 2
< 0.1%
10.3 2
< 0.1%
10.2 3
< 0.1%
10.1 2
< 0.1%
10 1
 
< 0.1%

loc_social_unlock_duration
Real number (ℝ)

MISSING  ZEROS 

Distinct10681
Distinct (%)82.3%
Missing21735
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean9.2636644
Minimum0
Maximum60
Zeros626
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:49.033654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00083368085
Q14.0627177
median7.6685142
Q312.516475
95-th percentile23.734848
Maximum60
Range60
Interquartile range (IQR)8.4537569

Descriptive statistics

Standard deviation7.6912288
Coefficient of variation (CV)0.83025771
Kurtosis5.5910691
Mean9.2636644
Median Absolute Deviation (MAD)4.0826301
Skewness1.8162216
Sum120158.99
Variance59.155
MonotonicityNot monotonic
2024-05-23T12:05:49.121623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 626
 
1.8%
4.400680187 6
 
< 0.1%
5.872907987 5
 
< 0.1%
3.585884125 5
 
< 0.1%
10.21287373 5
 
< 0.1%
8.694214784 5
 
< 0.1%
4.809586391 5
 
< 0.1%
16.55728423 5
 
< 0.1%
0.1449854106 5
 
< 0.1%
0.001249479384 5
 
< 0.1%
Other values (10671) 12299
35.4%
(Missing) 21735
62.6%
ValueCountFrequency (%)
0 626
1.8%
0.0002499791684 1
 
< 0.1%
0.00025 1
 
< 0.1%
0.0003333333333 1
 
< 0.1%
0.0003571428571 2
 
< 0.1%
0.0003571853792 1
 
< 0.1%
0.0004159733777 2
 
< 0.1%
0.0004161464836 1
 
< 0.1%
0.0004168982768 1
 
< 0.1%
0.0004544765945 1
 
< 0.1%
ValueCountFrequency (%)
60 5
< 0.1%
59.89777462 1
 
< 0.1%
59.35395315 1
 
< 0.1%
58.78488661 1
 
< 0.1%
57.57045991 1
 
< 0.1%
55.95076651 1
 
< 0.1%
55.94095002 1
 
< 0.1%
55.88306667 1
 
< 0.1%
55.78042494 2
 
< 0.1%
55.5412669 1
 
< 0.1%

loc_food_unlock_duration
Real number (ℝ)

MISSING  ZEROS 

Distinct14766
Distinct (%)91.4%
Missing18553
Missing (%)53.5%
Infinite0
Infinite (%)0.0%
Mean9.0076985
Minimum0
Maximum60
Zeros694
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:49.207574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.132072
Q14.2926623
median7.5210643
Q311.962121
95-th percentile21.994185
Maximum60
Range60
Interquartile range (IQR)7.6694587

Descriptive statistics

Standard deviation7.0941698
Coefficient of variation (CV)0.78756742
Kurtosis5.4102034
Mean9.0076985
Median Absolute Deviation (MAD)3.6650697
Skewness1.8218611
Sum145501.35
Variance50.327246
MonotonicityNot monotonic
2024-05-23T12:05:49.295734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 694
 
2.0%
0.001666666667 5
 
< 0.1%
2.210433882 4
 
< 0.1%
13.63873434 4
 
< 0.1%
3.139187549 4
 
< 0.1%
2.876171431 4
 
< 0.1%
33.79034303 4
 
< 0.1%
3.224869838 4
 
< 0.1%
18.69403 4
 
< 0.1%
4.635099505 4
 
< 0.1%
Other values (14756) 15422
44.4%
(Missing) 18553
53.5%
ValueCountFrequency (%)
0 694
2.0%
0.0002380385622 1
 
< 0.1%
0.0002470355731 1
 
< 0.1%
0.0002542803865 1
 
< 0.1%
0.0002774694784 1
 
< 0.1%
0.000322962644 1
 
< 0.1%
0.0003332963004 1
 
< 0.1%
0.0003335557038 1
 
< 0.1%
0.0004168982768 2
 
< 0.1%
0.0004544536457 1
 
< 0.1%
ValueCountFrequency (%)
60 2
< 0.1%
55.67813235 1
< 0.1%
55.33074752 1
< 0.1%
54.91969954 1
< 0.1%
53.22626606 1
< 0.1%
53.20917464 1
< 0.1%
53.12746059 1
< 0.1%
53.05245764 1
< 0.1%
52.94773165 1
< 0.1%
52.74420526 1
< 0.1%

loc_social_unlock_num
Real number (ℝ)

MISSING  ZEROS 

Distinct10099
Distinct (%)77.9%
Missing21735
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean7.9602969
Minimum0
Maximum87
Zeros626
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:49.384222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.42857143
Q14.1859341
median6.6666667
Q310.232554
95-th percentile19.468122
Maximum87
Range87
Interquartile range (IQR)6.0466195

Descriptive statistics

Standard deviation6.0621467
Coefficient of variation (CV)0.76154781
Kurtosis9.2952655
Mean7.9602969
Median Absolute Deviation (MAD)2.8873806
Skewness2.1248273
Sum103253.01
Variance36.749622
MonotonicityNot monotonic
2024-05-23T12:05:49.467390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 626
 
1.8%
6 60
 
0.2%
4 41
 
0.1%
2 25
 
0.1%
8 21
 
0.1%
12 20
 
0.1%
10 20
 
0.1%
3 17
 
< 0.1%
3.997779012 17
 
< 0.1%
2.4 14
 
< 0.1%
Other values (10089) 12110
34.9%
(Missing) 21735
62.6%
ValueCountFrequency (%)
0 626
1.8%
0.2182876546 1
 
< 0.1%
0.2307988204 1
 
< 0.1%
0.2392026578 1
 
< 0.1%
0.250034727 2
 
< 0.1%
0.2944671754 1
 
< 0.1%
0.299550674 1
 
< 0.1%
0.2999750021 1
 
< 0.1%
0.3 1
 
< 0.1%
0.300150075 2
 
< 0.1%
ValueCountFrequency (%)
87 1
< 0.1%
65.28127745 1
< 0.1%
64 1
< 0.1%
56.71069753 1
< 0.1%
56.59090909 1
< 0.1%
56 1
< 0.1%
55.07883705 1
< 0.1%
51.98555957 2
< 0.1%
49.51421354 1
< 0.1%
49.04001199 1
< 0.1%

loc_food_unlock_num
Real number (ℝ)

MISSING  ZEROS 

Distinct14580
Distinct (%)90.3%
Missing18553
Missing (%)53.5%
Infinite0
Infinite (%)0.0%
Mean7.5376589
Minimum0
Maximum72.015153
Zeros694
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:49.548073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.85714286
Q14.2777235
median6.6278867
Q39.7137298
95-th percentile17.147977
Maximum72.015153
Range72.015153
Interquartile range (IQR)5.4360064

Descriptive statistics

Standard deviation5.1047525
Coefficient of variation (CV)0.67723315
Kurtosis7.0861663
Mean7.5376589
Median Absolute Deviation (MAD)2.627746
Skewness1.7103021
Sum121755.8
Variance26.058498
MonotonicityNot monotonic
2024-05-23T12:05:49.637328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 694
 
2.0%
6 12
 
< 0.1%
4 9
 
< 0.1%
2 9
 
< 0.1%
8 9
 
< 0.1%
1.49937526 7
 
< 0.1%
14 7
 
< 0.1%
4.5 6
 
< 0.1%
1.998889506 6
 
< 0.1%
4.498125781 5
 
< 0.1%
Other values (14570) 15389
44.3%
(Missing) 18553
53.5%
ValueCountFrequency (%)
0 694
2.0%
0.08823096907 2
 
< 0.1%
0.1999666722 1
 
< 0.1%
0.2307988204 1
 
< 0.1%
0.2856462747 1
 
< 0.1%
0.2964426877 1
 
< 0.1%
0.2969337941 1
 
< 0.1%
0.3003003003 1
 
< 0.1%
0.3051364638 1
 
< 0.1%
0.3157833242 2
 
< 0.1%
ValueCountFrequency (%)
72.01515319 2
< 0.1%
52.9826534 1
< 0.1%
48.87651891 1
< 0.1%
45.42630843 1
< 0.1%
44.64696083 1
< 0.1%
42.38261284 1
< 0.1%
39.12778613 1
< 0.1%
38.15586765 1
< 0.1%
36.50716222 1
< 0.1%
36.22642651 1
< 0.1%

loc_social_still
Real number (ℝ)

MISSING 

Distinct11181
Distinct (%)86.2%
Missing21735
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean43.046375
Minimum0.15022534
Maximum59.997264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:49.724497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.15022534
5-th percentile24.241691
Q137.046742
median44.35616
Q350.305566
95-th percentile59.025406
Maximum59.997264
Range59.847039
Interquartile range (IQR)13.258825

Descriptive statistics

Standard deviation10.348318
Coefficient of variation (CV)0.24039929
Kurtosis0.95220226
Mean43.046375
Median Absolute Deviation (MAD)6.5279884
Skewness-0.8036675
Sum558354.53
Variance107.08768
MonotonicityNot monotonic
2024-05-23T12:05:49.812976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.96666667 10
 
< 0.1%
59.97501041 9
 
< 0.1%
38.53333333 7
 
< 0.1%
23.45977491 6
 
< 0.1%
59.98000666 6
 
< 0.1%
46.62220321 5
 
< 0.1%
37.04043351 5
 
< 0.1%
27.59839893 5
 
< 0.1%
59.97498958 5
 
< 0.1%
41.74732531 5
 
< 0.1%
Other values (11171) 12908
37.2%
(Missing) 21735
62.6%
ValueCountFrequency (%)
0.150225338 1
 
< 0.1%
0.3058445025 5
< 0.1%
0.6833333333 1
 
< 0.1%
0.9496043315 2
 
< 0.1%
1.075 1
 
< 0.1%
1.082130966 1
 
< 0.1%
1.2 2
 
< 0.1%
1.519350215 1
 
< 0.1%
1.616666667 1
 
< 0.1%
1.7125 1
 
< 0.1%
ValueCountFrequency (%)
59.9972639 3
< 0.1%
59.9958342 2
< 0.1%
59.99512274 1
 
< 0.1%
59.99497277 1
 
< 0.1%
59.9947373 2
< 0.1%
59.99460361 1
 
< 0.1%
59.99444496 1
 
< 0.1%
59.99401795 1
 
< 0.1%
59.99375098 1
 
< 0.1%
59.99375065 3
< 0.1%

loc_food_still
Real number (ℝ)

MISSING 

Distinct15400
Distinct (%)95.3%
Missing18553
Missing (%)53.5%
Infinite0
Infinite (%)0.0%
Mean44.963333
Minimum3.5081967
Maximum59.996624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:49.898797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.5081967
5-th percentile30.990209
Q140.077474
median45.663129
Q350.610687
95-th percentile58.058801
Maximum59.996624
Range56.488427
Interquartile range (IQR)10.533213

Descriptive statistics

Standard deviation8.2234008
Coefficient of variation (CV)0.18289127
Kurtosis1.1761166
Mean44.963333
Median Absolute Deviation (MAD)5.2117262
Skewness-0.66955149
Sum726292.71
Variance67.624321
MonotonicityNot monotonic
2024-05-23T12:05:49.982609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.97501041 6
 
< 0.1%
30.63259968 4
 
< 0.1%
44.74972191 4
 
< 0.1%
46.93 4
 
< 0.1%
59.96666667 4
 
< 0.1%
18.77698842 4
 
< 0.1%
42.72087546 4
 
< 0.1%
59.97496871 4
 
< 0.1%
51.23845195 4
 
< 0.1%
55.87110868 4
 
< 0.1%
Other values (15390) 16111
46.4%
(Missing) 18553
53.5%
ValueCountFrequency (%)
3.508196721 1
< 0.1%
4.264297612 1
< 0.1%
4.380366915 1
< 0.1%
4.625 2
< 0.1%
5.036319613 1
< 0.1%
5.370165746 1
< 0.1%
6.147438567 1
< 0.1%
6.866666667 1
< 0.1%
6.891891892 2
< 0.1%
7.206703911 1
< 0.1%
ValueCountFrequency (%)
59.99662371 1
< 0.1%
59.9947373 2
< 0.1%
59.99285672 2
< 0.1%
59.99248779 2
< 0.1%
59.99230769 1
< 0.1%
59.99142857 1
< 0.1%
59.99091184 1
< 0.1%
59.99064984 1
< 0.1%
59.99050846 1
< 0.1%
59.99030334 1
< 0.1%
Distinct25367
Distinct (%)73.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean8853.9435
Minimum0
Maximum43029.5
Zeros7479
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:50.068313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13983
median9494.1
Q313267.45
95-th percentile18520.345
Maximum43029.5
Range43029.5
Interquartile range (IQR)9284.45

Descriptive statistics

Standard deviation6157.9781
Coefficient of variation (CV)0.69550682
Kurtosis-0.7128497
Mean8853.9435
Median Absolute Deviation (MAD)4248.25
Skewness0.023173334
Sum3.0724955 × 108
Variance37920695
MonotonicityNot monotonic
2024-05-23T12:05:50.151459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7479
 
21.5%
11853.4 4
 
< 0.1%
13226.2 4
 
< 0.1%
11695.9 4
 
< 0.1%
10737.7 4
 
< 0.1%
11788.3 4
 
< 0.1%
13702 4
 
< 0.1%
12251.2 4
 
< 0.1%
14201 4
 
< 0.1%
12287.4 3
 
< 0.1%
Other values (25357) 27188
78.3%
(Missing) 4
 
< 0.1%
ValueCountFrequency (%)
0 7479
21.5%
0.5 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.9 1
 
< 0.1%
1.333333333 2
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 1
 
< 0.1%
3.833333333 1
 
< 0.1%
5.9 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
43029.5 1
< 0.1%
32758.1 1
< 0.1%
32734.5 1
< 0.1%
32095.9 1
< 0.1%
32066.1 1
< 0.1%
31683.75 1
< 0.1%
31503 1
< 0.1%
30850.7 1
< 0.1%
30841 1
< 0.1%
30607.9 1
< 0.1%

loc_social_dur_plus_loc_food_dur
Real number (ℝ)

ZEROS 

Distinct15229
Distinct (%)44.1%
Missing157
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.79524211
Minimum0
Maximum18.871861
Zeros18238
Zeros (%)52.5%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:50.233442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.1855833
95-th percentile3.269146
Maximum18.871861
Range18.871861
Interquartile range (IQR)1.1855833

Descriptive statistics

Standard deviation1.4832008
Coefficient of variation (CV)1.8650933
Kurtosis26.750526
Mean0.79524211
Median Absolute Deviation (MAD)0
Skewness4.1012815
Sum27474.82
Variance2.1998845
MonotonicityNot monotonic
2024-05-23T12:05:50.417959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18238
52.5%
0.05 38
 
0.1%
0.1 21
 
0.1%
0.06666666667 19
 
0.1%
0.1666944444 18
 
0.1%
0.08333333333 18
 
0.1%
0.06669444444 17
 
< 0.1%
0.1166666667 15
 
< 0.1%
0.1166944444 14
 
< 0.1%
0.08336111111 14
 
< 0.1%
Other values (15219) 16137
46.5%
(Missing) 157
 
0.5%
ValueCountFrequency (%)
0 18238
52.5%
0.01687447222 1
 
< 0.1%
0.03288888889 2
 
< 0.1%
0.03333333333 1
 
< 0.1%
0.03438888889 1
 
< 0.1%
0.03505555556 2
 
< 0.1%
0.03616666667 2
 
< 0.1%
0.03691358025 1
 
< 0.1%
0.037 1
 
< 0.1%
0.03763888889 2
 
< 0.1%
ValueCountFrequency (%)
18.87186111 1
< 0.1%
17.45444525 1
< 0.1%
17.37983333 1
< 0.1%
17.30280556 1
< 0.1%
17.23944444 1
< 0.1%
17.19877292 1
< 0.1%
16.61297222 1
< 0.1%
16.59935728 1
< 0.1%
16.39552106 1
< 0.1%
16.35594444 1
< 0.1%
Distinct12051
Distinct (%)94.1%
Missing21893
Missing (%)63.1%
Infinite0
Infinite (%)0.0%
Mean18.262271
Minimum0
Maximum111.01232
Zeros488
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:50.506045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7573766
Q19.5632145
median15.659121
Q323.98226
95-th percentile42.31483
Maximum111.01232
Range111.01232
Interquartile range (IQR)14.419045

Descriptive statistics

Standard deviation13.146578
Coefficient of variation (CV)0.71987644
Kurtosis5.612933
Mean18.262271
Median Absolute Deviation (MAD)6.8825456
Skewness1.7571063
Sum233994.47
Variance172.83252
MonotonicityNot monotonic
2024-05-23T12:05:50.593528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 488
 
1.4%
67.58068606 4
 
< 0.1%
22.80935972 3
 
< 0.1%
15.20585875 3
 
< 0.1%
8.632920806 3
 
< 0.1%
6.202359696 3
 
< 0.1%
9.631693853 3
 
< 0.1%
11.30765981 3
 
< 0.1%
12.2558594 3
 
< 0.1%
11.0804495 3
 
< 0.1%
Other values (12041) 12297
35.4%
(Missing) 21893
63.1%
ValueCountFrequency (%)
0 488
1.4%
0.0005832754688 1
 
< 0.1%
0.0006934428561 1
 
< 0.1%
0.001080368906 1
 
< 0.1%
0.001248959201 1
 
< 0.1%
0.001850361296 1
 
< 0.1%
0.002448271888 1
 
< 0.1%
0.002498958767 1
 
< 0.1%
0.002504173623 2
 
< 0.1%
0.002857142857 2
 
< 0.1%
ValueCountFrequency (%)
111.0123181 1
< 0.1%
106.2549212 1
< 0.1%
105 1
< 0.1%
104.9695478 1
< 0.1%
104.9624468 1
< 0.1%
104.4065027 1
< 0.1%
102.4643149 1
< 0.1%
101.749634 1
< 0.1%
101.5340972 1
< 0.1%
101.0911477 1
< 0.1%

loc_social_unlock_num_plus_loc_food_unlock_num
Real number (ℝ)

MISSING  ZEROS 

Distinct12041
Distinct (%)94.0%
Missing21893
Missing (%)63.1%
Infinite0
Infinite (%)0.0%
Mean15.501724
Minimum0
Maximum116.53621
Zeros488
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:50.678291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4511021
Q18.9240836
median13.488781
Q319.690134
95-th percentile35.275748
Maximum116.53621
Range116.53621
Interquartile range (IQR)10.76605

Descriptive statistics

Standard deviation10.33105
Coefficient of variation (CV)0.66644523
Kurtosis5.8788897
Mean15.501724
Median Absolute Deviation (MAD)5.1449554
Skewness1.7412343
Sum198623.59
Variance106.7306
MonotonicityNot monotonic
2024-05-23T12:05:50.770282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 488
 
1.4%
2.998750521 4
 
< 0.1%
12 4
 
< 0.1%
23.60583501 4
 
< 0.1%
4 4
 
< 0.1%
16 4
 
< 0.1%
18.75775591 3
 
< 0.1%
15.55941592 3
 
< 0.1%
6.681657536 3
 
< 0.1%
22.54280945 3
 
< 0.1%
Other values (12031) 12293
35.4%
(Missing) 21893
63.1%
ValueCountFrequency (%)
0 488
1.4%
0.4345522985 1
 
< 0.1%
0.4615976407 1
 
< 0.1%
0.610860825 1
 
< 0.1%
0.611710942 1
 
< 0.1%
0.6700685222 1
 
< 0.1%
0.6999305626 1
 
< 0.1%
0.8321314273 1
 
< 0.1%
0.8505837123 1
 
< 0.1%
0.8736580183 1
 
< 0.1%
ValueCountFrequency (%)
116.5362144 1
< 0.1%
108.9826534 1
< 0.1%
96.16405622 1
< 0.1%
89.56124241 1
< 0.1%
88.16779811 1
< 0.1%
87.8783296 1
< 0.1%
85.0130825 1
< 0.1%
84.76522568 1
< 0.1%
83.3837672 1
< 0.1%
82.89742985 1
< 0.1%

loc_social_still_plus_loc_food_still
Real number (ℝ)

MISSING 

Distinct12527
Distinct (%)97.8%
Missing21893
Missing (%)63.1%
Infinite0
Infinite (%)0.0%
Mean88.11687
Minimum9.25
Maximum119.98947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-23T12:05:50.859736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9.25
5-th percentile59.708762
Q178.140865
median89.620535
Q398.98401
95-th percentile113.277
Maximum119.98947
Range110.73947
Interquartile range (IQR)20.843145

Descriptive statistics

Standard deviation16.264549
Coefficient of variation (CV)0.18457928
Kurtosis0.7007249
Mean88.11687
Median Absolute Deviation (MAD)10.333419
Skewness-0.50761046
Sum1129041.4
Variance264.53555
MonotonicityNot monotonic
2024-05-23T12:05:50.946497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.42622951 4
 
< 0.1%
99.11082248 3
 
< 0.1%
103.577807 3
 
< 0.1%
89.39682889 3
 
< 0.1%
91.51461009 3
 
< 0.1%
75.65994963 3
 
< 0.1%
91.42767429 3
 
< 0.1%
41.38300751 3
 
< 0.1%
32.51212034 3
 
< 0.1%
74.68912653 3
 
< 0.1%
Other values (12517) 12782
36.8%
(Missing) 21893
63.1%
ValueCountFrequency (%)
9.25 2
< 0.1%
10.11622825 1
< 0.1%
12.29487713 1
< 0.1%
13.67585752 1
< 0.1%
16.78531702 1
< 0.1%
17.41982156 1
< 0.1%
18.02159249 1
< 0.1%
18.0672994 2
< 0.1%
19.92716018 1
< 0.1%
20.022116 1
< 0.1%
ValueCountFrequency (%)
119.9894746 2
< 0.1%
119.9886909 2
< 0.1%
119.9825235 1
< 0.1%
119.982339 1
< 0.1%
119.9815993 1
< 0.1%
119.9800645 1
< 0.1%
119.9793505 1
< 0.1%
119.9788286 1
< 0.1%
119.9785113 1
< 0.1%
119.9783486 1
< 0.1%

Interactions

2024-05-23T12:05:42.292685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:03.537557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:05.221878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:06.806510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:08.320026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:09.802642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:11.410418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:12.971232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:14.539498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:16.351112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:18.056644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:20.796061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:22.494379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:24.156302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:25.655753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:27.926633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:29.649794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:31.119585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:32.839812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:34.350059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:35.922447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:37.444260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:39.111051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:40.639275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:42.359749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:03.651422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:05.290730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:06.874141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:08.385625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:09.868204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:11.488901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:13.035910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:14.611504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:16.435518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:18.126086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:20.918039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:22.558075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:24.223657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:25.722220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:28.039242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:29.715312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:31.284371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:32.909902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:34.413847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:35.993197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:37.512535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:39.177376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:40.703746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:42.422430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:03.774368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:05.347777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:06.932417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:08.447000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:10.019543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:11.565174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:13.095887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:14.681855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:16.506104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:18.191397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:21.013113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:22.619937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:24.287229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:25.785393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:28.145410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:29.776307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:31.348430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:32.974104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:34.477004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:36.051488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:37.575508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:39.242376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:40.768326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:42.485647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:03.856478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:05.404424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:06.989576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:08.506168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:10.076067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:11.631380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:13.153805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:14.750702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:16.576042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:18.253045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:21.117196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:22.680314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:24.345525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:25.851169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:28.246123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:29.838860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:31.413771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:33.037009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:34.538719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:36.109897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:37.636551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:39.306239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:40.834464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:42.644315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:03.922954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:05.468553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:07.050263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:08.563844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:10.135067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:11.694652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:13.213715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:14.827819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:16.644730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:18.315086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:21.197228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:22.747308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:24.404968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:25.911486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:28.345557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:29.896920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:31.475843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:33.098780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:34.597385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:36.171428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:37.700331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:39.369141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:40.896517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:42.701540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:03.982801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:05.525922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:07.106005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:08.621747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:10.189076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:11.753671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:13.269572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:14.902169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:16.702948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:18.417524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:21.266698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:22.923096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-05-23T12:05:11.270338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:12.844681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:14.419701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:16.174317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:17.924338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:20.486780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:22.368997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:24.024809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:25.528107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:27.716417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:29.525052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:30.997489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:32.704233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:34.224895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:35.800097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:37.312570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:38.979356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:40.511420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:42.165073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:43.834876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:05.155785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:06.744185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:08.258123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:09.742065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:11.341515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:12.908592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:14.481277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:16.273649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:17.992661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:20.657741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:22.433434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:24.092525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:25.594397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:27.820349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:29.589059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:31.060770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:32.770207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:34.288164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:35.863024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:37.379182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:39.045780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:40.575917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-23T12:05:42.230407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-05-23T12:05:43.952174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-23T12:05:44.259830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-23T12:05:44.774455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

uidgenderracecohort_yearis_iosday_surveyact_on_foot_ep_0act_running_ep_0act_walking_ep_0loc_study_stillloc_study_durloc_study_productloc_home_durloc_workout_duract_in_vehicle_ep_0loc_dist_ep_0loc_social_durloc_food_durloc_visit_num_ep_0loc_social_unlock_durationloc_food_unlock_durationloc_social_unlock_numloc_food_unlock_numloc_social_stillloc_food_stillact_running_ep_0_plus_act_walking_ep_0loc_social_dur_plus_loc_food_durloc_social_unlock_duration_plus_loc_food_unlock_durationloc_social_unlock_num_plus_loc_food_unlock_numloc_social_still_plus_loc_food_still
03569e2f520db9014b4acc4227a6421c1bothwhite2017.002017-09-094167.0000000.00.0NaNNaNNaNNaNNaN561.0000002767.287472NaNNaN6.0NaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaN
13569e2f520db9014b4acc4227a6421c1bothwhite2017.002017-09-103330.5000000.00.0NaNNaNNaNNaNNaN694.5000002737.717474NaNNaN4.5NaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaN
23569e2f520db9014b4acc4227a6421c1bothwhite2017.002017-09-144133.8333330.00.043.7980405.671473248.69540914.1607180.710007483.1666675548.6118300.1684611.7282985.549.94604720.8570427.9148106.73272854.32739640.4822110.01.89675970.80308914.64753894.809606
33569e2f520db9014b4acc4227a6421c1bothwhite2017.002017-09-184027.8000000.00.047.1403954.395330235.18303214.9691091.645656357.5000007865.0605480.1937381.1297645.532.98322321.2012337.4835567.78362454.47917840.1912230.01.32350254.18445715.26717994.670401
43569e2f520db9014b4acc4227a6421c1bothwhite2017.002017-09-224235.8000000.00.041.4311884.228635191.89006215.2822541.636484308.0000008556.0293420.7833981.1917446.017.69897721.1728085.2293236.59975839.82942836.1045590.01.97514338.87178511.82908075.933988
53569e2f520db9014b4acc4227a6421c1bothwhite2017.002017-09-244344.2000000.00.040.6553693.687871184.29188713.4165021.811246713.30000014766.2314520.7328601.0729376.411.24956316.9920524.6922256.12627736.92983536.7263370.01.80579728.24161610.81850273.656172
63569e2f520db9014b4acc4227a6421c1bothwhite2017.002017-09-274065.8000000.00.040.1747753.133734151.25157812.4574811.155785862.50000019906.3980511.1260841.7451455.913.45601614.9089136.7172925.54417935.44534836.1584080.02.87122928.36492912.26147171.603756
73569e2f520db9014b4acc4227a6421c1bothwhite2017.002017-09-304298.9000000.00.038.7324883.017122142.35949610.7916180.855095849.90000018808.5369900.8007431.5372056.616.12764217.5506997.0224265.49257534.82933036.1685080.02.33794833.67834012.51500170.997838
83569e2f520db9014b4acc4227a6421c1bothwhite2017.002017-10-014131.5000000.00.040.1306303.051682149.24838710.6880680.687200896.40000018723.6767770.7328171.6397056.419.71935019.1405678.0419295.63794537.33420839.0795390.02.37252338.85991713.67987476.413747
93569e2f520db9014b4acc4227a6421c1bothwhite2017.002017-10-064369.3000000.00.044.4070093.637883180.47961912.7745710.187365384.1000006135.3131190.3896901.8103886.020.18987921.5384915.8291954.60390835.16310540.5572650.02.20007941.72837010.43310275.720371
uidgenderracecohort_yearis_iosday_surveyact_on_foot_ep_0act_running_ep_0act_walking_ep_0loc_study_stillloc_study_durloc_study_productloc_home_durloc_workout_duract_in_vehicle_ep_0loc_dist_ep_0loc_social_durloc_food_durloc_visit_num_ep_0loc_social_unlock_durationloc_food_unlock_durationloc_social_unlock_numloc_food_unlock_numloc_social_stillloc_food_stillact_running_ep_0_plus_act_walking_ep_0loc_social_dur_plus_loc_food_durloc_social_unlock_duration_plus_loc_food_unlock_durationloc_social_unlock_num_plus_loc_food_unlock_numloc_social_still_plus_loc_food_still
346962c4f43b2212eee5ba69563f139911138Mwhite2018.012020-09-16NaN288.311407.1NaN0.0NaN2.5193060.07504.8211140.9248180.0000000.00.5NaNNaNNaNNaNNaNNaN11695.40.000000NaNNaNNaN
346972c4f43b2212eee5ba69563f139911138Mwhite2018.012020-09-24NaN296.610999.2NaN0.0NaN0.0000000.061.14131.1991010.0000000.00.9NaNNaNNaNNaNNaNNaN11295.80.000000NaNNaNNaN
346982c4f43b2212eee5ba69563f139911138Mwhite2018.012020-09-26NaN256.010560.2NaN0.0NaN0.0000000.0324.17439.4678050.0000000.01.3NaNNaNNaNNaNNaNNaN10816.20.000000NaNNaNNaN
346992c4f43b2212eee5ba69563f139911138Mwhite2018.012020-10-10NaN88.312300.3NaN0.0NaN0.0000000.0817.214194.9486870.1834440.02.75.530055NaN7.161071NaN26.44106NaN12388.60.183444NaNNaNNaN
347002c4f43b2212eee5ba69563f139911138Mwhite2018.012020-10-14NaN79.811025.6NaN0.0NaN1.0161670.01918.333832.9111970.1834440.02.85.530055NaN7.161071NaN26.44106NaN11105.40.183444NaNNaNNaN
347012c4f43b2212eee5ba69563f139911138Mwhite2018.012020-12-24NaN22.822748.5NaN0.0NaN8.4938890.03879.767682.6349930.0000000.02.5NaNNaNNaNNaNNaNNaN22771.30.000000NaNNaNNaN
347028617ddac1f48b148e3683738519b2e7aNaNNaN2018.01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
34703ea716dd032aaa0dcf8bfa36b1811917fNaNNaN2017.01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
34704df5e798581def8d477316520953b9171NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
34705e6d71fe4a3c10b075ae1cf51a2fe6cfdNaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN